Artificial Intelligence and National Security

As technology advances at an exponential rate, it is becoming increasingly important to consider the ramifications of that technology in the geopolitical environment, and especially as it pertains to American national security. One of the most important categories of technological innovation that will likely disrupt the global balance of geopolitical power, especially along the US-China axis, is the advent and growing sophistication of artificial intelligence. In order to address the new and evolving national security challenges that will accompany this disruption, this paper seeks to define and explain the disparity in artificial intelligence capabilities between the United States and China. First, it will describe the contemporary situation regarding the AI capabilities of both China and the United States, as well the implications of those capabilities as they relate to American national security interests. Additionally, this paper will identify the major contributing factors that are driving and/or mitigating artificial intelligence development in each country. Moreover, this paper will explain the discrepancies found to exist between the two countries in terms of the discrepancies found between their contributing and mitigating factors. Lastly, this paper will discuss the possible implications of these findings for the national security of the United States

FedDev Ontario’s ARC Initiatives OCAD University Project# 1 – Haptic holography

Through innovative haptic and holography application, Haptic Holography focused on developing a more realistic and accurate three-dimensional (3D) ‘synthetic reality’ for purposes of pre-commercialization to enhance current medical training

African trypanosomiasis: Synthesis & SAR enabling novel drug discovery of ubiquinol mimics for trypanosome alternative oxidase

African trypanosomiasis is a parasitic disease affecting 5000 humans and millions of livestock animals in sub-Saharan Africa every year. Current treatments are limited, difficult to administer and often toxic causing long term injury or death in many patients. Trypanosome alternative oxidase is a parasite specific enzyme whose inhibition by the natural product ascofuranone (AF) has been shown to be curative in murine models. Until now synthetic methods to AF analogues have been limited, this has restricted both understanding of the key structural features required for binding and also how this chemotype could be developed to an effective therapeutic agent. The development of 3 amenable novel synthetic routes to ascofuranone-like compounds is described. The SAR generated around the AF chemotype is reported with correlation to the inhibition of T. b. brucei growth and corresponding selectivity in cytotoxic assessment in mammalian HepG2 cell lines. These methods allow access to greater synthetic diversification and have enabled the synthesis of compounds that have and will continue to facilitate further optimisation of the AF chemotype into a drug-like lead

Mesophotic.org: a repository for scientific information on mesophotic ecosystems

Mesophotic coral ecosystems (MCEs) and temperate mesophotic ecosystems (TMEs) occur at depths of roughly 30-150\ua0m depth and are characterized by the presence of photosynthetic organisms despite reduced light availability. Exploration of these ecosystems dates back several decades, but our knowledge remained extremely limited until about a decade ago, when a renewed interest resulted in the establishment of a rapidly growing research community. Here, we present the 'mesophotic.org' database, a comprehensive and curated repository of scientific literature on mesophotic ecosystems. Through both manually curated and automatically extracted metadata, the repository facilitates rapid retrieval of available information about particular topics (e.g. taxa or geographic regions), exploration of spatial/temporal trends in research and identification of knowledge gaps. The repository can be queried to comprehensively obtain available data to address large-scale questions and guide future research directions. Overall, the 'mesophotic.org' repository provides an independent and open-source platform for the ever-growing research community working on MCEs and TMEs to collate and expedite our understanding of the occurrence, composition and functioning of these ecosystems. Database URL: http://mesophotic.org/

How is model-related uncertainty quantified and reported in different disciplines?

How do we know how much we know? Quantifying uncertainty associated with our modelling work is the only way we can answer how much we know about any phenomenon. With quantitative science now highly influential in the public sphere and the results from models translating into action, we must support our conclusions with sufficient rigour to produce useful, reproducible results. Incomplete consideration of model-based uncertainties can lead to false conclusions with real world impacts. Despite these potentially damaging consequences, uncertainty consideration is incomplete both within and across scientific fields. We take a unique interdisciplinary approach and conduct a systematic audit of model-related uncertainty quantification from seven scientific fields, spanning the biological, physical, and social sciences. Our results show no single field is achieving complete consideration of model uncertainties, but together we can fill the gaps. We propose opportunities to improve the quantification of uncertainty through use of a source framework for uncertainty consideration, model type specific guidelines, improved presentation, and shared best practice. We also identify shared outstanding challenges (uncertainty in input data, balancing trade-offs, error propagation, and defining how much uncertainty is required). Finally, we make nine concrete recommendations for current practice (following good practice guidelines and an uncertainty checklist, presenting uncertainty numerically, and propagating model-related uncertainty into conclusions), future research priorities (uncertainty in input data, quantifying uncertainty in complex models, and the importance of missing uncertainty in different contexts), and general research standards across the sciences (transparency about study limitations and dedicated uncertainty sections of manuscripts).Comment: 40 Pages (including supporting information), 3 Figures, 2 Boxes, 1 Tabl

Insights into the quantification and reporting of model-related uncertainty across different disciplines

Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with real world impacts in diverse spheres, including conservation, epidemiology, climate science, and policy. Despite these potentially damaging consequences, we still know little about how different fields quantify and report uncertainty. We introduce the "sources of uncertainty" framework, using it to conduct a systematic audit of model-related uncertainty quantification from seven scientific fields, spanning the biological, physical, and political sciences. Our interdisciplinary audit shows no field fully considers all possible sources of uncertainty, but each has its own best practices alongside shared outstanding challenges. We make ten easy-to-implement recommendations to improve the consistency, completeness, and clarity of reporting on model-related uncertainty. These recommendations serve as a guide to best practices across scientific fields and expand our toolbox for high-quality research